From 9ad0f0e6e8ac75eac50f5e21be1941079d8326e9 Mon Sep 17 00:00:00 2001 From: James Betker Date: Tue, 22 Mar 2022 11:52:46 -0600 Subject: [PATCH] Modifications to support "v1.5" --- do_tts.py | 72 ++-- models/autoregressive.py | 21 +- models/diffusion_decoder.py | 598 +++++++++++++++++++++++++++ models/discrete_diffusion_vocoder.py | 468 --------------------- models/dvae.py | 390 ----------------- models/vocoder.py | 325 +++++++++++++++ requirements.txt | 3 +- utils/audio.py | 86 +++- utils/diffusion.py | 18 + utils/stft.py | 193 +++++++++ 10 files changed, 1279 insertions(+), 895 deletions(-) create mode 100644 models/diffusion_decoder.py delete mode 100644 models/discrete_diffusion_vocoder.py delete mode 100644 models/dvae.py create mode 100644 models/vocoder.py create mode 100644 utils/stft.py diff --git a/do_tts.py b/do_tts.py index 8b22bfb..a3587c1 100644 --- a/do_tts.py +++ b/do_tts.py @@ -8,14 +8,14 @@ import torch.nn.functional as F import torchaudio import progressbar -from models.dvae import DiscreteVAE +from models.diffusion_decoder import DiffusionTts from models.autoregressive import UnifiedVoice from tqdm import tqdm from models.arch_util import TorchMelSpectrogram -from models.discrete_diffusion_vocoder import DiscreteDiffusionVocoder from models.text_voice_clip import VoiceCLIP -from utils.audio import load_audio +from models.vocoder import UnivNetGenerator +from utils.audio import load_audio, wav_to_univnet_mel, denormalize_tacotron_mel from utils.diffusion import SpacedDiffusion, space_timesteps, get_named_beta_schedule from utils.tokenizer import VoiceBpeTokenizer @@ -23,7 +23,6 @@ pbar = None def download_models(): MODELS = { 'clip.pth': 'https://huggingface.co/jbetker/tortoise-tts-clip/resolve/main/pytorch-model.bin', - 'dvae.pth': 'https://huggingface.co/jbetker/voice-dvae/resolve/main/pytorch_model.bin', 'diffusion.pth': 'https://huggingface.co/jbetker/tortoise-tts-diffusion-v1/resolve/main/pytorch-model.bin', 'autoregressive.pth': 'https://huggingface.co/jbetker/tortoise-tts-autoregressive/resolve/main/pytorch-model.bin' } @@ -47,12 +46,14 @@ def download_models(): request.urlretrieve(url, f'.models/{model_name}', show_progress) print('Done.') + def load_discrete_vocoder_diffuser(trained_diffusion_steps=4000, desired_diffusion_steps=200): """ Helper function to load a GaussianDiffusion instance configured for use as a vocoder. """ return SpacedDiffusion(use_timesteps=space_timesteps(trained_diffusion_steps, [desired_diffusion_steps]), model_mean_type='epsilon', - model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps)) + model_var_type='learned_range', loss_type='mse', betas=get_named_beta_schedule('linear', trained_diffusion_steps), + conditioning_free=True, conditioning_free_k=1) def load_conditioning(path, sample_rate=22050, cond_length=132300): @@ -94,26 +95,26 @@ def fix_autoregressive_output(codes, stop_token): return codes -def do_spectrogram_diffusion(diffusion_model, dvae_model, diffuser, mel_codes, conditioning_input, spectrogram_compression_factor=128, mean=False): +def do_spectrogram_diffusion(diffusion_model, diffuser, mel_codes, conditioning_input, mean=False): """ Uses the specified diffusion model and DVAE model to convert the provided MEL & conditioning inputs into an audio clip. """ with torch.no_grad(): - mel = dvae_model.decode(mel_codes)[0] - - # Pad MEL to multiples of 2048//spectrogram_compression_factor - msl = mel.shape[-1] - dsl = 2048 // spectrogram_compression_factor + cond_mel = wav_to_univnet_mel(conditioning_input.squeeze(1), do_normalization=False) + # Pad MEL to multiples of 32 + msl = mel_codes.shape[-1] + dsl = 32 gap = dsl - (msl % dsl) if gap > 0: - mel = torch.nn.functional.pad(mel, (0, gap)) + mel = torch.nn.functional.pad(mel_codes, (0, gap)) - output_shape = (mel.shape[0], 1, mel.shape[-1] * spectrogram_compression_factor) + output_shape = (mel.shape[0], 100, mel.shape[-1]*4) if mean: - return diffuser.p_sample_loop(diffusion_model, output_shape, noise=torch.zeros(output_shape, device=mel_codes.device), - model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input}) + mel = diffuser.p_sample_loop(diffusion_model, output_shape, noise=torch.zeros(output_shape, device=mel_codes.device), + model_kwargs={'aligned_conditioning': mel_codes, 'conditioning_input': cond_mel}) else: - return diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'spectrogram': mel, 'conditioning_input': conditioning_input}) + mel = diffuser.p_sample_loop(diffusion_model, output_shape, model_kwargs={'aligned_conditioning': mel_codes, 'conditioning_input': cond_mel}) + return denormalize_tacotron_mel(mel)[:,:,:msl*4] if __name__ == '__main__': @@ -145,12 +146,6 @@ if __name__ == '__main__': download_models() for voice in args.voice.split(','): - print("Loading GPT TTS..") - autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024, - heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False).cuda().eval() - autoregressive.load_state_dict(torch.load('.models/autoregressive.pth')) - stop_mel_token = autoregressive.stop_mel_token - print("Loading data..") tokenizer = VoiceBpeTokenizer() text = torch.IntTensor(tokenizer.encode(args.text)).unsqueeze(0).cuda() @@ -160,7 +155,15 @@ if __name__ == '__main__': for cond_path in cond_paths: c, cond_wav = load_conditioning(cond_path) conds.append(c) - conds = torch.stack(conds, dim=1) # And just use the last cond_wav for the diffusion model. + conds = torch.stack(conds, dim=1) + cond_diffusion = cond_wav[:, :88200] # The diffusion model expects <= 88200 conditioning samples. + + print("Loading GPT TTS..") + autoregressive = UnifiedVoice(max_mel_tokens=300, max_text_tokens=200, max_conditioning_inputs=2, layers=30, model_dim=1024, + heads=16, number_text_tokens=256, start_text_token=255, checkpointing=False, train_solo_embeddings=False, + average_conditioning_embeddings=True).cuda().eval() + autoregressive.load_state_dict(torch.load('.models/autoregressive.pth')) + stop_mel_token = autoregressive.stop_mel_token with torch.no_grad(): print("Performing autoregressive inference..") @@ -194,20 +197,25 @@ if __name__ == '__main__': # Delete the autoregressive and clip models to free up GPU memory del samples, clip - print("Loading DVAE..") - dvae = DiscreteVAE(positional_dims=1, channels=80, hidden_dim=512, num_resnet_blocks=3, codebook_dim=512, num_tokens=8192, num_layers=2, - record_codes=True, kernel_size=3, use_transposed_convs=False).cuda().eval() - dvae.load_state_dict(torch.load('.models/dvae.pth'), strict=False) print("Loading Diffusion Model..") - diffusion = DiscreteDiffusionVocoder(model_channels=128, dvae_dim=80, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], num_res_blocks=[1, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1], - spectrogram_conditioning_resolutions=[2,512], attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2, - conditioning_inputs_provided=True, time_embed_dim_multiplier=4).cuda().eval() + diffusion = DiffusionTts(model_channels=512, in_channels=100, out_channels=200, in_latent_channels=1024, + channel_mult=[1, 2, 3, 4], num_res_blocks=[3, 3, 3, 3], token_conditioning_resolutions=[1,4,8], + dropout=0, attention_resolutions=[4,8], num_heads=8, kernel_size=3, scale_factor=2, + time_embed_dim_multiplier=4, unconditioned_percentage=0, conditioning_dim_factor=2, + conditioning_expansion=1) diffusion.load_state_dict(torch.load('.models/diffusion.pth')) + diffusion = diffusion.cuda().eval() + print("Loading vocoder..") + vocoder = UnivNetGenerator() + vocoder.load_state_dict(torch.load('.models/vocoder.pth')['model_g']) + vocoder = vocoder.cuda() + vocoder.eval(inference=True) diffuser = load_discrete_vocoder_diffuser(desired_diffusion_steps=100) print("Performing vocoding..") # Perform vocoding on each batch element separately: The diffusion model is very memory (and compute!) intensive. for b in range(best_results.shape[0]): code = best_results[b].unsqueeze(0) - wav = do_spectrogram_diffusion(diffusion, dvae, diffuser, code, cond_wav, spectrogram_compression_factor=256, mean=True) - torchaudio.save(os.path.join(args.output_path, f'{voice}_{b}.wav'), wav.squeeze(0).cpu(), 22050) + mel = do_spectrogram_diffusion(diffusion, diffuser, code, cond_diffusion, mean=False) + wav = vocoder.inference(mel) + torchaudio.save(os.path.join(args.output_path, f'{voice}_{b}.wav'), wav.squeeze(0).cpu(), 24000) diff --git a/models/autoregressive.py b/models/autoregressive.py index b513246..c1dea14 100644 --- a/models/autoregressive.py +++ b/models/autoregressive.py @@ -192,7 +192,8 @@ class ConditioningEncoder(nn.Module): embedding_dim, attn_blocks=6, num_attn_heads=4, - do_checkpointing=False): + do_checkpointing=False, + mean=False): super().__init__() attn = [] self.init = nn.Conv1d(spec_dim, embedding_dim, kernel_size=1) @@ -201,11 +202,15 @@ class ConditioningEncoder(nn.Module): self.attn = nn.Sequential(*attn) self.dim = embedding_dim self.do_checkpointing = do_checkpointing + self.mean = mean def forward(self, x): h = self.init(x) h = self.attn(h) - return h[:, :, 0] + if self.mean: + return h.mean(dim=2) + else: + return h[:, :, 0] class LearnedPositionEmbeddings(nn.Module): @@ -275,7 +280,7 @@ class UnifiedVoice(nn.Module): mel_length_compression=1024, number_text_tokens=256, start_text_token=255, stop_text_token=0, number_mel_codes=8194, start_mel_token=8192, stop_mel_token=8193, train_solo_embeddings=False, use_mel_codes_as_input=True, - checkpointing=True): + checkpointing=True, average_conditioning_embeddings=False): """ Args: layers: Number of layers in transformer stack. @@ -294,6 +299,7 @@ class UnifiedVoice(nn.Module): train_solo_embeddings: use_mel_codes_as_input: checkpointing: + average_conditioning_embeddings: Whether or not conditioning embeddings should be averaged, instead of fed piecewise into the model. """ super().__init__() @@ -311,6 +317,7 @@ class UnifiedVoice(nn.Module): self.max_conditioning_inputs = max_conditioning_inputs self.mel_length_compression = mel_length_compression self.conditioning_encoder = ConditioningEncoder(80, model_dim, num_attn_heads=heads) + self.average_conditioning_embeddings = average_conditioning_embeddings self.text_embedding = nn.Embedding(self.number_text_tokens, model_dim) if use_mel_codes_as_input: self.mel_embedding = nn.Embedding(self.number_mel_codes, model_dim) @@ -408,6 +415,8 @@ class UnifiedVoice(nn.Module): for j in range(speech_conditioning_input.shape[1]): conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) conds = torch.stack(conds, dim=1) + if self.average_conditioning_embeddings: + conds = conds.mean(dim=1).unsqueeze(1) text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token) text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) @@ -446,6 +455,8 @@ class UnifiedVoice(nn.Module): for j in range(speech_conditioning_input.shape[1]): conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) conds = torch.stack(conds, dim=1) + if self.average_conditioning_embeddings: + conds = conds.mean(dim=1).unsqueeze(1) text_inputs, text_targets = self.build_aligned_inputs_and_targets(text_inputs, self.start_text_token, self.stop_text_token) text_emb = self.text_embedding(text_inputs) + self.text_pos_embedding(text_inputs) + self.text_solo_embedding @@ -472,6 +483,8 @@ class UnifiedVoice(nn.Module): for j in range(speech_conditioning_input.shape[1]): conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) conds = torch.stack(conds, dim=1) + if self.average_conditioning_embeddings: + conds = conds.mean(dim=1).unsqueeze(1) mel_codes, mel_targets = self.build_aligned_inputs_and_targets(mel_codes, self.start_mel_token, self.stop_mel_token) if raw_mels is not None: @@ -508,6 +521,8 @@ class UnifiedVoice(nn.Module): for j in range(speech_conditioning_input.shape[1]): conds.append(self.conditioning_encoder(speech_conditioning_input[:, j])) conds = torch.stack(conds, dim=1) + if self.average_conditioning_embeddings: + conds = conds.mean(dim=1).unsqueeze(1) emb = torch.cat([conds, text_emb], dim=1) self.inference_model.store_mel_emb(emb) diff --git a/models/diffusion_decoder.py b/models/diffusion_decoder.py new file mode 100644 index 0000000..c946663 --- /dev/null +++ b/models/diffusion_decoder.py @@ -0,0 +1,598 @@ +""" +This model is based on OpenAI's UNet from improved diffusion, with modifications to support a MEL conditioning signal +and an audio conditioning input. It has also been simplified somewhat. +Credit: https://github.com/openai/improved-diffusion +""" +import functools +import math +from abc import abstractmethod + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch import autocast +from torch.nn import Linear +from torch.utils.checkpoint import checkpoint +from x_transformers import ContinuousTransformerWrapper, Encoder + +from models.arch_util import normalization, zero_module, Downsample, Upsample, AudioMiniEncoder, AttentionBlock + + +def is_latent(t): + return t.dtype == torch.float + + +def is_sequence(t): + return t.dtype == torch.long + + +def ceil_multiple(base, multiple): + res = base % multiple + if res == 0: + return base + return base + (multiple - res) + + +def timestep_embedding(timesteps, dim, max_period=10000): + """ + Create sinusoidal timestep embeddings. + + :param timesteps: a 1-D Tensor of N indices, one per batch element. + These may be fractional. + :param dim: the dimension of the output. + :param max_period: controls the minimum frequency of the embeddings. + :return: an [N x dim] Tensor of positional embeddings. + """ + half = dim // 2 + freqs = torch.exp( + -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half + ).to(device=timesteps.device) + args = timesteps[:, None].float() * freqs[None] + embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) + if dim % 2: + embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) + return embedding + + +class TimestepBlock(nn.Module): + """ + Any module where forward() takes timestep embeddings as a second argument. + """ + + @abstractmethod + def forward(self, x, emb): + """ + Apply the module to `x` given `emb` timestep embeddings. + """ + + +class TimestepEmbedSequential(nn.Sequential, TimestepBlock): + """ + A sequential module that passes timestep embeddings to the children that + support it as an extra input. + """ + + def forward(self, x, emb): + for layer in self: + if isinstance(layer, TimestepBlock): + x = layer(x, emb) + else: + x = layer(x) + return x + + +class ResBlock(TimestepBlock): + def __init__( + self, + channels, + emb_channels, + dropout, + out_channels=None, + kernel_size=3, + efficient_config=True, + use_scale_shift_norm=False, + ): + super().__init__() + self.channels = channels + self.emb_channels = emb_channels + self.dropout = dropout + self.out_channels = out_channels or channels + self.use_scale_shift_norm = use_scale_shift_norm + padding = {1: 0, 3: 1, 5: 2}[kernel_size] + eff_kernel = 1 if efficient_config else 3 + eff_padding = 0 if efficient_config else 1 + + self.in_layers = nn.Sequential( + normalization(channels), + nn.SiLU(), + nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding), + ) + + self.emb_layers = nn.Sequential( + nn.SiLU(), + Linear( + emb_channels, + 2 * self.out_channels if use_scale_shift_norm else self.out_channels, + ), + ) + self.out_layers = nn.Sequential( + normalization(self.out_channels), + nn.SiLU(), + nn.Dropout(p=dropout), + zero_module( + nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding) + ), + ) + + if self.out_channels == channels: + self.skip_connection = nn.Identity() + else: + self.skip_connection = nn.Conv1d(channels, self.out_channels, eff_kernel, padding=eff_padding) + + def forward(self, x, emb): + """ + Apply the block to a Tensor, conditioned on a timestep embedding. + + :param x: an [N x C x ...] Tensor of features. + :param emb: an [N x emb_channels] Tensor of timestep embeddings. + :return: an [N x C x ...] Tensor of outputs. + """ + return checkpoint( + self._forward, x, emb + ) + + def _forward(self, x, emb): + h = self.in_layers(x) + emb_out = self.emb_layers(emb).type(h.dtype) + while len(emb_out.shape) < len(h.shape): + emb_out = emb_out[..., None] + if self.use_scale_shift_norm: + out_norm, out_rest = self.out_layers[0], self.out_layers[1:] + scale, shift = torch.chunk(emb_out, 2, dim=1) + h = out_norm(h) * (1 + scale) + shift + h = out_rest(h) + else: + h = h + emb_out + h = self.out_layers(h) + return self.skip_connection(x) + h + + +class CheckpointedLayer(nn.Module): + """ + Wraps a module. When forward() is called, passes kwargs that require_grad through torch.checkpoint() and bypasses + checkpoint for all other args. + """ + def __init__(self, wrap): + super().__init__() + self.wrap = wrap + + def forward(self, x, *args, **kwargs): + for k, v in kwargs.items(): + assert not (isinstance(v, torch.Tensor) and v.requires_grad) # This would screw up checkpointing. + partial = functools.partial(self.wrap, **kwargs) + return torch.utils.checkpoint.checkpoint(partial, x, *args) + + +class CheckpointedXTransformerEncoder(nn.Module): + """ + Wraps a ContinuousTransformerWrapper and applies CheckpointedLayer to each layer and permutes from channels-mid + to channels-last that XTransformer expects. + """ + def __init__(self, needs_permute=True, **xtransformer_kwargs): + super().__init__() + self.transformer = ContinuousTransformerWrapper(**xtransformer_kwargs) + self.needs_permute = needs_permute + + for i in range(len(self.transformer.attn_layers.layers)): + n, b, r = self.transformer.attn_layers.layers[i] + self.transformer.attn_layers.layers[i] = nn.ModuleList([n, CheckpointedLayer(b), r]) + + def forward(self, x, **kwargs): + if self.needs_permute: + x = x.permute(0,2,1) + h = self.transformer(x, **kwargs) + return h.permute(0,2,1) + + +class DiffusionTts(nn.Module): + """ + The full UNet model with attention and timestep embedding. + + Customized to be conditioned on an aligned prior derived from a autoregressive + GPT-style model. + + :param in_channels: channels in the input Tensor. + :param in_latent_channels: channels from the input latent. + :param model_channels: base channel count for the model. + :param out_channels: channels in the output Tensor. + :param num_res_blocks: number of residual blocks per downsample. + :param attention_resolutions: a collection of downsample rates at which + attention will take place. May be a set, list, or tuple. + For example, if this contains 4, then at 4x downsampling, attention + will be used. + :param dropout: the dropout probability. + :param channel_mult: channel multiplier for each level of the UNet. + :param conv_resample: if True, use learned convolutions for upsampling and + downsampling. + :param num_heads: the number of attention heads in each attention layer. + :param num_heads_channels: if specified, ignore num_heads and instead use + a fixed channel width per attention head. + :param num_heads_upsample: works with num_heads to set a different number + of heads for upsampling. Deprecated. + :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. + :param resblock_updown: use residual blocks for up/downsampling. + :param use_new_attention_order: use a different attention pattern for potentially + increased efficiency. + """ + + def __init__( + self, + model_channels, + in_channels=1, + in_latent_channels=1024, + in_tokens=8193, + conditioning_dim_factor=8, + conditioning_expansion=4, + out_channels=2, # mean and variance + dropout=0, + # res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K + channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48), + num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2), + # spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0) + # attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 + token_conditioning_resolutions=(1,16,), + attention_resolutions=(512,1024,2048), + conv_resample=True, + use_fp16=False, + num_heads=1, + num_head_channels=-1, + num_heads_upsample=-1, + kernel_size=3, + scale_factor=2, + time_embed_dim_multiplier=4, + freeze_main_net=False, + efficient_convs=True, # Uses kernels with width of 1 in several places rather than 3. + use_scale_shift_norm=True, + # Parameters for regularization. + unconditioned_percentage=.1, # This implements a mechanism similar to what is used in classifier-free training. + # Parameters for super-sampling. + super_sampling=False, + super_sampling_max_noising_factor=.1, + ): + super().__init__() + + if num_heads_upsample == -1: + num_heads_upsample = num_heads + + if super_sampling: + in_channels *= 2 # In super-sampling mode, the LR input is concatenated directly onto the input. + self.in_channels = in_channels + self.model_channels = model_channels + self.out_channels = out_channels + self.attention_resolutions = attention_resolutions + self.dropout = dropout + self.channel_mult = channel_mult + self.conv_resample = conv_resample + self.num_heads = num_heads + self.num_head_channels = num_head_channels + self.num_heads_upsample = num_heads_upsample + self.super_sampling_enabled = super_sampling + self.super_sampling_max_noising_factor = super_sampling_max_noising_factor + self.unconditioned_percentage = unconditioned_percentage + self.enable_fp16 = use_fp16 + self.alignment_size = 2 ** (len(channel_mult)+1) + self.freeze_main_net = freeze_main_net + padding = 1 if kernel_size == 3 else 2 + down_kernel = 1 if efficient_convs else 3 + + time_embed_dim = model_channels * time_embed_dim_multiplier + self.time_embed = nn.Sequential( + Linear(model_channels, time_embed_dim), + nn.SiLU(), + Linear(time_embed_dim, time_embed_dim), + ) + + conditioning_dim = model_channels * conditioning_dim_factor + # Either code_converter or latent_converter is used, depending on what type of conditioning data is fed. + # This model is meant to be able to be trained on both for efficiency purposes - it is far less computationally + # complex to generate tokens, while generating latents will normally mean propagating through a deep autoregressive + # transformer network. + self.code_converter = nn.Sequential( + nn.Embedding(in_tokens, conditioning_dim), + CheckpointedXTransformerEncoder( + needs_permute=False, + max_seq_len=-1, + use_pos_emb=False, + attn_layers=Encoder( + dim=conditioning_dim, + depth=3, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_emb_dim=True, + ) + )) + self.latent_converter = nn.Conv1d(in_latent_channels, conditioning_dim, 1) + self.aligned_latent_padding_embedding = nn.Parameter(torch.randn(1,in_latent_channels,1)) + if in_channels > 60: # It's a spectrogram. + self.contextual_embedder = nn.Sequential(nn.Conv1d(in_channels,conditioning_dim,3,padding=1,stride=2), + CheckpointedXTransformerEncoder( + needs_permute=True, + max_seq_len=-1, + use_pos_emb=False, + attn_layers=Encoder( + dim=conditioning_dim, + depth=4, + heads=num_heads, + ff_dropout=dropout, + attn_dropout=dropout, + use_rmsnorm=True, + ff_glu=True, + rotary_emb_dim=True, + ) + )) + else: + self.contextual_embedder = AudioMiniEncoder(1, conditioning_dim, base_channels=32, depth=6, resnet_blocks=1, + attn_blocks=3, num_attn_heads=8, dropout=dropout, downsample_factor=4, kernel_size=5) + self.conditioning_conv = nn.Conv1d(conditioning_dim*2, conditioning_dim, 1) + self.unconditioned_embedding = nn.Parameter(torch.randn(1,conditioning_dim,1)) + self.conditioning_timestep_integrator = TimestepEmbedSequential( + ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm), + AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels), + ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm), + AttentionBlock(conditioning_dim, num_heads=num_heads, num_head_channels=num_head_channels), + ResBlock(conditioning_dim, time_embed_dim, dropout, out_channels=conditioning_dim, kernel_size=1, use_scale_shift_norm=use_scale_shift_norm), + ) + self.conditioning_expansion = conditioning_expansion + + self.input_blocks = nn.ModuleList( + [ + TimestepEmbedSequential( + nn.Conv1d(in_channels, model_channels, kernel_size, padding=padding) + ) + ] + ) + token_conditioning_blocks = [] + self._feature_size = model_channels + input_block_chans = [model_channels] + ch = model_channels + ds = 1 + + for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): + if ds in token_conditioning_resolutions: + token_conditioning_block = nn.Conv1d(conditioning_dim, ch, 1) + token_conditioning_block.weight.data *= .02 + self.input_blocks.append(token_conditioning_block) + token_conditioning_blocks.append(token_conditioning_block) + + for _ in range(num_blocks): + layers = [ + ResBlock( + ch, + time_embed_dim, + dropout, + out_channels=int(mult * model_channels), + kernel_size=kernel_size, + efficient_config=efficient_convs, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = int(mult * model_channels) + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + ) + ) + self.input_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + input_block_chans.append(ch) + if level != len(channel_mult) - 1: + out_ch = ch + self.input_blocks.append( + TimestepEmbedSequential( + Downsample( + ch, conv_resample, out_channels=out_ch, factor=scale_factor, ksize=down_kernel, pad=0 if down_kernel == 1 else 1 + ) + ) + ) + ch = out_ch + input_block_chans.append(ch) + ds *= 2 + self._feature_size += ch + + self.middle_block = TimestepEmbedSequential( + ResBlock( + ch, + time_embed_dim, + dropout, + kernel_size=kernel_size, + efficient_config=efficient_convs, + use_scale_shift_norm=use_scale_shift_norm, + ), + AttentionBlock( + ch, + num_heads=num_heads, + num_head_channels=num_head_channels, + ), + ResBlock( + ch, + time_embed_dim, + dropout, + kernel_size=kernel_size, + efficient_config=efficient_convs, + use_scale_shift_norm=use_scale_shift_norm, + ), + ) + self._feature_size += ch + + self.output_blocks = nn.ModuleList([]) + for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]: + for i in range(num_blocks + 1): + ich = input_block_chans.pop() + layers = [ + ResBlock( + ch + ich, + time_embed_dim, + dropout, + out_channels=int(model_channels * mult), + kernel_size=kernel_size, + efficient_config=efficient_convs, + use_scale_shift_norm=use_scale_shift_norm, + ) + ] + ch = int(model_channels * mult) + if ds in attention_resolutions: + layers.append( + AttentionBlock( + ch, + num_heads=num_heads_upsample, + num_head_channels=num_head_channels, + ) + ) + if level and i == num_blocks: + out_ch = ch + layers.append( + Upsample(ch, conv_resample, out_channels=out_ch, factor=scale_factor) + ) + ds //= 2 + self.output_blocks.append(TimestepEmbedSequential(*layers)) + self._feature_size += ch + + self.out = nn.Sequential( + normalization(ch), + nn.SiLU(), + zero_module(nn.Conv1d(model_channels, out_channels, kernel_size, padding=padding)), + ) + + def fix_alignment(self, x, aligned_conditioning): + """ + The UNet requires that the input is a certain multiple of 2, defined by the UNet depth. Enforce this by + padding both and before forward propagation and removing the padding before returning. + """ + cm = ceil_multiple(x.shape[-1], self.alignment_size) + if cm != 0: + pc = (cm-x.shape[-1])/x.shape[-1] + x = F.pad(x, (0,cm-x.shape[-1])) + # Also fix aligned_latent, which is aligned to x. + if is_latent(aligned_conditioning): + aligned_conditioning = torch.cat([aligned_conditioning, + self.aligned_latent_padding_embedding.repeat(x.shape[0], 1, int(pc * aligned_conditioning.shape[-1]))], dim=-1) + else: + aligned_conditioning = F.pad(aligned_conditioning, (0, int(pc*aligned_conditioning.shape[-1]))) + return x, aligned_conditioning + + def forward(self, x, timesteps, aligned_conditioning, conditioning_input, lr_input=None, conditioning_free=False): + """ + Apply the model to an input batch. + + :param x: an [N x C x ...] Tensor of inputs. + :param timesteps: a 1-D batch of timesteps. + :param aligned_conditioning: an aligned latent or sequence of tokens providing useful data about the sample to be produced. + :param conditioning_input: a full-resolution audio clip that is used as a reference to the style you want decoded. + :param lr_input: for super-sampling models, a guidance audio clip at a lower sampling rate. + :param conditioning_free: When set, all conditioning inputs (including tokens and conditioning_input) will not be considered. + :return: an [N x C x ...] Tensor of outputs. + """ + assert conditioning_input is not None + if self.super_sampling_enabled: + assert lr_input is not None + if self.training and self.super_sampling_max_noising_factor > 0: + noising_factor = random.uniform(0,self.super_sampling_max_noising_factor) + lr_input = torch.randn_like(lr_input) * noising_factor + lr_input + lr_input = F.interpolate(lr_input, size=(x.shape[-1],), mode='nearest') + x = torch.cat([x, lr_input], dim=1) + + # Shuffle aligned_latent to BxCxS format + if is_latent(aligned_conditioning): + aligned_conditioning = aligned_conditioning.permute(0, 2, 1) + + # Fix input size to the proper multiple of 2 so we don't get alignment errors going down and back up the U-net. + orig_x_shape = x.shape[-1] + x, aligned_conditioning = self.fix_alignment(x, aligned_conditioning) + + with autocast(x.device.type, enabled=self.enable_fp16): + hs = [] + time_emb = self.time_embed(timestep_embedding(timesteps, self.model_channels)) + + # Note: this block does not need to repeated on inference, since it is not timestep-dependent. + if conditioning_free: + code_emb = self.unconditioned_embedding.repeat(x.shape[0], 1, 1) + else: + cond_emb = self.contextual_embedder(conditioning_input) + if len(cond_emb.shape) == 3: # Just take the first element. + cond_emb = cond_emb[:, :, 0] + if is_latent(aligned_conditioning): + code_emb = self.latent_converter(aligned_conditioning) + else: + code_emb = self.code_converter(aligned_conditioning) + cond_emb = cond_emb.unsqueeze(-1).repeat(1, 1, code_emb.shape[-1]) + code_emb = self.conditioning_conv(torch.cat([cond_emb, code_emb], dim=1)) + # Mask out the conditioning branch for whole batch elements, implementing something similar to classifier-free guidance. + if self.training and self.unconditioned_percentage > 0: + unconditioned_batches = torch.rand((code_emb.shape[0], 1, 1), + device=code_emb.device) < self.unconditioned_percentage + code_emb = torch.where(unconditioned_batches, self.unconditioned_embedding.repeat(x.shape[0], 1, 1), + code_emb) + + # Everything after this comment is timestep dependent. + code_emb = torch.repeat_interleave(code_emb, self.conditioning_expansion, dim=-1) + code_emb = self.conditioning_timestep_integrator(code_emb, time_emb) + + first = True + time_emb = time_emb.float() + h = x + for k, module in enumerate(self.input_blocks): + if isinstance(module, nn.Conv1d): + h_tok = F.interpolate(module(code_emb), size=(h.shape[-1]), mode='nearest') + h = h + h_tok + else: + with autocast(x.device.type, enabled=self.enable_fp16 and not first): + # First block has autocast disabled to allow a high precision signal to be properly vectorized. + h = module(h, time_emb) + hs.append(h) + first = False + h = self.middle_block(h, time_emb) + for module in self.output_blocks: + h = torch.cat([h, hs.pop()], dim=1) + h = module(h, time_emb) + + # Last block also has autocast disabled for high-precision outputs. + h = h.float() + out = self.out(h) + + # Involve probabilistic or possibly unused parameters in loss so we don't get DDP errors. + extraneous_addition = 0 + params = [self.aligned_latent_padding_embedding, self.unconditioned_embedding] + list(self.latent_converter.parameters()) + for p in params: + extraneous_addition = extraneous_addition + p.mean() + out = out + extraneous_addition * 0 + + return out[:, :, :orig_x_shape] + + +if __name__ == '__main__': + clip = torch.randn(2, 1, 32868) + aligned_latent = torch.randn(2,388,1024) + aligned_sequence = torch.randint(0,8192,(2,388)) + cond = torch.randn(2, 1, 44000) + ts = torch.LongTensor([600, 600]) + model = DiffusionTts(128, + channel_mult=[1,1.5,2, 3, 4, 6, 8], + num_res_blocks=[2, 2, 2, 2, 2, 2, 1], + token_conditioning_resolutions=[1,4,16,64], + attention_resolutions=[], + num_heads=8, + kernel_size=3, + scale_factor=2, + time_embed_dim_multiplier=4, + super_sampling=False, + efficient_convs=False) + # Test with latent aligned conditioning + o = model(clip, ts, aligned_latent, cond) + # Test with sequence aligned conditioning + o = model(clip, ts, aligned_sequence, cond) diff --git a/models/discrete_diffusion_vocoder.py b/models/discrete_diffusion_vocoder.py deleted file mode 100644 index 6fe6053..0000000 --- a/models/discrete_diffusion_vocoder.py +++ /dev/null @@ -1,468 +0,0 @@ -""" -This model is based on OpenAI's UNet from improved diffusion, with modifications to support a MEL conditioning signal -and an audio conditioning input. It has also been simplified somewhat. -Credit: https://github.com/openai/improved-diffusion -""" - - -import math -from abc import abstractmethod - -import torch -import torch.nn as nn - -from models.arch_util import normalization, zero_module, Downsample, Upsample, AudioMiniEncoder, AttentionBlock - - -def timestep_embedding(timesteps, dim, max_period=10000): - """ - Create sinusoidal timestep embeddings. - - :param timesteps: a 1-D Tensor of N indices, one per batch element. - These may be fractional. - :param dim: the dimension of the output. - :param max_period: controls the minimum frequency of the embeddings. - :return: an [N x dim] Tensor of positional embeddings. - """ - half = dim // 2 - freqs = torch.exp( - -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half - ).to(device=timesteps.device) - args = timesteps[:, None].float() * freqs[None] - embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) - if dim % 2: - embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) - return embedding - - -class TimestepBlock(nn.Module): - """ - Any module where forward() takes timestep embeddings as a second argument. - """ - - @abstractmethod - def forward(self, x, emb): - """ - Apply the module to `x` given `emb` timestep embeddings. - """ - - -class TimestepEmbedSequential(nn.Sequential, TimestepBlock): - """ - A sequential module that passes timestep embeddings to the children that - support it as an extra input. - """ - - def forward(self, x, emb): - for layer in self: - if isinstance(layer, TimestepBlock): - x = layer(x, emb) - else: - x = layer(x) - return x - - -class TimestepResBlock(TimestepBlock): - """ - A residual block that can optionally change the number of channels. - - :param channels: the number of input channels. - :param emb_channels: the number of timestep embedding channels. - :param dropout: the rate of dropout. - :param out_channels: if specified, the number of out channels. - :param use_conv: if True and out_channels is specified, use a spatial - convolution instead of a smaller 1x1 convolution to change the - channels in the skip connection. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param up: if True, use this block for upsampling. - :param down: if True, use this block for downsampling. - """ - - def __init__( - self, - channels, - emb_channels, - dropout, - out_channels=None, - use_conv=False, - use_scale_shift_norm=False, - up=False, - down=False, - kernel_size=3, - ): - super().__init__() - self.channels = channels - self.emb_channels = emb_channels - self.dropout = dropout - self.out_channels = out_channels or channels - self.use_conv = use_conv - self.use_scale_shift_norm = use_scale_shift_norm - padding = 1 if kernel_size == 3 else (2 if kernel_size == 5 else 0) - - self.in_layers = nn.Sequential( - normalization(channels), - nn.SiLU(), - nn.Conv1d(channels, self.out_channels, kernel_size, padding=padding), - ) - - self.updown = up or down - - if up: - self.h_upd = Upsample(channels, False, dims) - self.x_upd = Upsample(channels, False, dims) - elif down: - self.h_upd = Downsample(channels, False, dims) - self.x_upd = Downsample(channels, False, dims) - else: - self.h_upd = self.x_upd = nn.Identity() - - self.emb_layers = nn.Sequential( - nn.SiLU(), - nn.Linear( - emb_channels, - 2 * self.out_channels if use_scale_shift_norm else self.out_channels, - ), - ) - self.out_layers = nn.Sequential( - normalization(self.out_channels), - nn.SiLU(), - nn.Dropout(p=dropout), - zero_module( - nn.Conv1d(self.out_channels, self.out_channels, kernel_size, padding=padding) - ), - ) - - if self.out_channels == channels: - self.skip_connection = nn.Identity() - elif use_conv: - self.skip_connection = nn.Conv1d( - channels, self.out_channels, kernel_size, padding=padding - ) - else: - self.skip_connection = nn.Conv1d(channels, self.out_channels, 1) - - def forward(self, x, emb): - if self.updown: - in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1] - h = in_rest(x) - h = self.h_upd(h) - x = self.x_upd(x) - h = in_conv(h) - else: - h = self.in_layers(x) - emb_out = self.emb_layers(emb).type(h.dtype) - while len(emb_out.shape) < len(h.shape): - emb_out = emb_out[..., None] - if self.use_scale_shift_norm: - out_norm, out_rest = self.out_layers[0], self.out_layers[1:] - scale, shift = torch.chunk(emb_out, 2, dim=1) - h = out_norm(h) * (1 + scale) + shift - h = out_rest(h) - else: - h = h + emb_out - h = self.out_layers(h) - return self.skip_connection(x) + h - - -class DiscreteSpectrogramConditioningBlock(nn.Module): - def __init__(self, dvae_channels, channels, level): - super().__init__() - self.intg = nn.Sequential(nn.Conv1d(dvae_channels, channels, kernel_size=1), - normalization(channels), - nn.SiLU(), - nn.Conv1d(channels, channels, kernel_size=3)) - self.level = level - - """ - Embeds the given codes and concatenates them onto x. Return shape is the same as x.shape. - - :param x: bxcxS waveform latent - :param codes: bxN discrete codes, N <= S - """ - def forward(self, x, dvae_in): - b, c, S = x.shape - _, q, N = dvae_in.shape - emb = self.intg(dvae_in) - emb = nn.functional.interpolate(emb, size=(S,), mode='nearest') - return torch.cat([x, emb], dim=1) - - -class DiscreteDiffusionVocoder(nn.Module): - """ - The full UNet model with attention and timestep embedding. - - Customized to be conditioned on a spectrogram prior. - - :param in_channels: channels in the input Tensor. - :param spectrogram_channels: channels in the conditioning spectrogram. - :param model_channels: base channel count for the model. - :param out_channels: channels in the output Tensor. - :param num_res_blocks: number of residual blocks per downsample. - :param attention_resolutions: a collection of downsample rates at which - attention will take place. May be a set, list, or tuple. - For example, if this contains 4, then at 4x downsampling, attention - will be used. - :param dropout: the dropout probability. - :param channel_mult: channel multiplier for each level of the UNet. - :param conv_resample: if True, use learned convolutions for upsampling and - downsampling. - :param dims: determines if the signal is 1D, 2D, or 3D. - :param num_heads: the number of attention heads in each attention layer. - :param num_heads_channels: if specified, ignore num_heads and instead use - a fixed channel width per attention head. - :param num_heads_upsample: works with num_heads to set a different number - of heads for upsampling. Deprecated. - :param use_scale_shift_norm: use a FiLM-like conditioning mechanism. - :param resblock_updown: use residual blocks for up/downsampling. - :param use_new_attention_order: use a different attention pattern for potentially - increased efficiency. - """ - - def __init__( - self, - model_channels, - in_channels=1, - out_channels=2, # mean and variance - dvae_dim=512, - dropout=0, - # res 1, 2, 4, 8,16,32,64,128,256,512, 1K, 2K - channel_mult= (1,1.5,2, 3, 4, 6, 8, 12, 16, 24, 32, 48), - num_res_blocks=(1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2), - # spec_cond: 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0) - # attn: 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1 - spectrogram_conditioning_resolutions=(512,), - attention_resolutions=(512,1024,2048), - conv_resample=True, - dims=1, - use_fp16=False, - num_heads=1, - num_head_channels=-1, - num_heads_upsample=-1, - use_scale_shift_norm=False, - resblock_updown=False, - kernel_size=3, - scale_factor=2, - conditioning_inputs_provided=True, - time_embed_dim_multiplier=4, - ): - super().__init__() - - if num_heads_upsample == -1: - num_heads_upsample = num_heads - - self.in_channels = in_channels - self.model_channels = model_channels - self.out_channels = out_channels - self.attention_resolutions = attention_resolutions - self.dropout = dropout - self.channel_mult = channel_mult - self.conv_resample = conv_resample - self.dtype = torch.float16 if use_fp16 else torch.float32 - self.num_heads = num_heads - self.num_head_channels = num_head_channels - self.num_heads_upsample = num_heads_upsample - self.dims = dims - - padding = 1 if kernel_size == 3 else 2 - - time_embed_dim = model_channels * time_embed_dim_multiplier - self.time_embed = nn.Sequential( - nn.Linear(model_channels, time_embed_dim), - nn.SiLU(), - nn.Linear(time_embed_dim, time_embed_dim), - ) - - self.conditioning_enabled = conditioning_inputs_provided - if conditioning_inputs_provided: - self.contextual_embedder = AudioMiniEncoder(in_channels, time_embed_dim, base_channels=32, depth=6, resnet_blocks=1, - attn_blocks=2, num_attn_heads=2, dropout=dropout, downsample_factor=4, kernel_size=5) - - seqlyr = TimestepEmbedSequential( - nn.Conv1d(in_channels, model_channels, kernel_size, padding=padding) - ) - seqlyr.level = 0 - self.input_blocks = nn.ModuleList([seqlyr]) - spectrogram_blocks = [] - self._feature_size = model_channels - input_block_chans = [model_channels] - ch = model_channels - ds = 1 - - for level, (mult, num_blocks) in enumerate(zip(channel_mult, num_res_blocks)): - if ds in spectrogram_conditioning_resolutions: - spec_cond_block = DiscreteSpectrogramConditioningBlock(dvae_dim, ch, 2 ** level) - self.input_blocks.append(spec_cond_block) - spectrogram_blocks.append(spec_cond_block) - ch *= 2 - - for _ in range(num_blocks): - layers = [ - TimestepResBlock( - ch, - time_embed_dim, - dropout, - out_channels=int(mult * model_channels), - use_scale_shift_norm=use_scale_shift_norm, - kernel_size=kernel_size, - ) - ] - ch = int(mult * model_channels) - if ds in attention_resolutions: - layers.append( - AttentionBlock( - ch, - num_heads=num_heads, - num_head_channels=num_head_channels, - ) - ) - layer = TimestepEmbedSequential(*layers) - layer.level = 2 ** level - self.input_blocks.append(layer) - self._feature_size += ch - input_block_chans.append(ch) - if level != len(channel_mult) - 1: - out_ch = ch - upblk = TimestepEmbedSequential( - TimestepResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - use_scale_shift_norm=use_scale_shift_norm, - down=True, - kernel_size=kernel_size, - ) - if resblock_updown - else Downsample( - ch, conv_resample, out_channels=out_ch, factor=scale_factor - ) - ) - upblk.level = 2 ** level - self.input_blocks.append(upblk) - ch = out_ch - input_block_chans.append(ch) - ds *= 2 - self._feature_size += ch - - self.middle_block = TimestepEmbedSequential( - TimestepResBlock( - ch, - time_embed_dim, - dropout, - use_scale_shift_norm=use_scale_shift_norm, - kernel_size=kernel_size, - ), - AttentionBlock( - ch, - num_heads=num_heads, - num_head_channels=num_head_channels, - ), - TimestepResBlock( - ch, - time_embed_dim, - dropout, - use_scale_shift_norm=use_scale_shift_norm, - kernel_size=kernel_size, - ), - ) - self._feature_size += ch - - self.output_blocks = nn.ModuleList([]) - for level, (mult, num_blocks) in list(enumerate(zip(channel_mult, num_res_blocks)))[::-1]: - for i in range(num_blocks + 1): - ich = input_block_chans.pop() - layers = [ - TimestepResBlock( - ch + ich, - time_embed_dim, - dropout, - out_channels=int(model_channels * mult), - use_scale_shift_norm=use_scale_shift_norm, - kernel_size=kernel_size, - ) - ] - ch = int(model_channels * mult) - if ds in attention_resolutions: - layers.append( - AttentionBlock( - ch, - num_heads=num_heads_upsample, - num_head_channels=num_head_channels, - ) - ) - if level and i == num_blocks: - out_ch = ch - layers.append( - TimestepResBlock( - ch, - time_embed_dim, - dropout, - out_channels=out_ch, - use_scale_shift_norm=use_scale_shift_norm, - up=True, - kernel_size=kernel_size, - ) - if resblock_updown - else Upsample(ch, conv_resample, out_channels=out_ch, factor=scale_factor) - ) - ds //= 2 - layer = TimestepEmbedSequential(*layers) - layer.level = 2 ** level - self.output_blocks.append(layer) - self._feature_size += ch - - self.out = nn.Sequential( - normalization(ch), - nn.SiLU(), - zero_module(nn.Conv1d(model_channels, out_channels, kernel_size, padding=padding)), - ) - - def forward(self, x, timesteps, spectrogram, conditioning_input=None): - """ - Apply the model to an input batch. - - :param x: an [N x C x ...] Tensor of inputs. - :param timesteps: a 1-D batch of timesteps. - :param y: an [N] Tensor of labels, if class-conditional. - :return: an [N x C x ...] Tensor of outputs. - """ - assert x.shape[-1] % 2048 == 0 # This model operates at base//2048 at it's bottom levels, thus this requirement. - if self.conditioning_enabled: - assert conditioning_input is not None - - hs = [] - emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels)) - if self.conditioning_enabled: - emb2 = self.contextual_embedder(conditioning_input) - emb = emb1 + emb2 - else: - emb = emb1 - - h = x.type(self.dtype) - for k, module in enumerate(self.input_blocks): - if isinstance(module, DiscreteSpectrogramConditioningBlock): - h = module(h, spectrogram) - else: - h = module(h, emb) - hs.append(h) - h = self.middle_block(h, emb) - for module in self.output_blocks: - h = torch.cat([h, hs.pop()], dim=1) - h = module(h, emb) - h = h.type(x.dtype) - return self.out(h) - - -# Test for ~4 second audio clip at 22050Hz -if __name__ == '__main__': - clip = torch.randn(2, 1, 40960) - spec = torch.randn(2,80,160) - cond = torch.randn(2, 1, 40960) - ts = torch.LongTensor([555, 556]) - model = DiscreteDiffusionVocoder(model_channels=128, channel_mult=[1, 1, 1.5, 2, 3, 4, 6, 8, 8, 8, 8], - num_res_blocks=[1,2, 2, 2, 2, 2, 2, 2, 2, 1, 1 ], spectrogram_conditioning_resolutions=[2,512], - dropout=.05, attention_resolutions=[512,1024], num_heads=4, kernel_size=3, scale_factor=2, - conditioning_inputs_provided=True, conditioning_input_dim=80, time_embed_dim_multiplier=4, - dvae_dim=80) - - print(model(clip, ts, spec, cond).shape) diff --git a/models/dvae.py b/models/dvae.py deleted file mode 100644 index 3465ba6..0000000 --- a/models/dvae.py +++ /dev/null @@ -1,390 +0,0 @@ -import functools -from math import sqrt - -import torch -import torch.distributed as distributed -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange - - -def default(val, d): - return val if val is not None else d - - -def eval_decorator(fn): - def inner(model, *args, **kwargs): - was_training = model.training - model.eval() - out = fn(model, *args, **kwargs) - model.train(was_training) - return out - return inner - - -# Quantizer implemented by the rosinality vqvae repo. -# Credit: https://github.com/rosinality/vq-vae-2-pytorch -class Quantize(nn.Module): - def __init__(self, dim, n_embed, decay=0.99, eps=1e-5, balancing_heuristic=False, new_return_order=False): - super().__init__() - - self.dim = dim - self.n_embed = n_embed - self.decay = decay - self.eps = eps - - self.balancing_heuristic = balancing_heuristic - self.codes = None - self.max_codes = 64000 - self.codes_full = False - self.new_return_order = new_return_order - - embed = torch.randn(dim, n_embed) - self.register_buffer("embed", embed) - self.register_buffer("cluster_size", torch.zeros(n_embed)) - self.register_buffer("embed_avg", embed.clone()) - - def forward(self, input, return_soft_codes=False): - if self.balancing_heuristic and self.codes_full: - h = torch.histc(self.codes, bins=self.n_embed, min=0, max=self.n_embed) / len(self.codes) - mask = torch.logical_or(h > .9, h < .01).unsqueeze(1) - ep = self.embed.permute(1,0) - ea = self.embed_avg.permute(1,0) - rand_embed = torch.randn_like(ep) * mask - self.embed = (ep * ~mask + rand_embed).permute(1,0) - self.embed_avg = (ea * ~mask + rand_embed).permute(1,0) - self.cluster_size = self.cluster_size * ~mask.squeeze() - if torch.any(mask): - print(f"Reset {torch.sum(mask)} embedding codes.") - self.codes = None - self.codes_full = False - - flatten = input.reshape(-1, self.dim) - dist = ( - flatten.pow(2).sum(1, keepdim=True) - - 2 * flatten @ self.embed - + self.embed.pow(2).sum(0, keepdim=True) - ) - soft_codes = -dist - _, embed_ind = soft_codes.max(1) - embed_onehot = F.one_hot(embed_ind, self.n_embed).type(flatten.dtype) - embed_ind = embed_ind.view(*input.shape[:-1]) - quantize = self.embed_code(embed_ind) - - if self.balancing_heuristic: - if self.codes is None: - self.codes = embed_ind.flatten() - else: - self.codes = torch.cat([self.codes, embed_ind.flatten()]) - if len(self.codes) > self.max_codes: - self.codes = self.codes[-self.max_codes:] - self.codes_full = True - - if self.training: - embed_onehot_sum = embed_onehot.sum(0) - embed_sum = flatten.transpose(0, 1) @ embed_onehot - - if distributed.is_initialized() and distributed.get_world_size() > 1: - distributed.all_reduce(embed_onehot_sum) - distributed.all_reduce(embed_sum) - - self.cluster_size.data.mul_(self.decay).add_( - embed_onehot_sum, alpha=1 - self.decay - ) - self.embed_avg.data.mul_(self.decay).add_(embed_sum, alpha=1 - self.decay) - n = self.cluster_size.sum() - cluster_size = ( - (self.cluster_size + self.eps) / (n + self.n_embed * self.eps) * n - ) - embed_normalized = self.embed_avg / cluster_size.unsqueeze(0) - self.embed.data.copy_(embed_normalized) - - diff = (quantize.detach() - input).pow(2).mean() - quantize = input + (quantize - input).detach() - - if return_soft_codes: - return quantize, diff, embed_ind, soft_codes.view(input.shape[:-1] + (-1,)) - elif self.new_return_order: - return quantize, embed_ind, diff - else: - return quantize, diff, embed_ind - - def embed_code(self, embed_id): - return F.embedding(embed_id, self.embed.transpose(0, 1)) - - -# Fits a soft-discretized input to a normal-PDF across the specified dimension. -# In other words, attempts to force the discretization function to have a mean equal utilization across all discrete -# values with the specified expected variance. -class DiscretizationLoss(nn.Module): - def __init__(self, discrete_bins, dim, expected_variance, store_past=0): - super().__init__() - self.discrete_bins = discrete_bins - self.dim = dim - self.dist = torch.distributions.Normal(0, scale=expected_variance) - if store_past > 0: - self.record_past = True - self.register_buffer("accumulator_index", torch.zeros(1, dtype=torch.long, device='cpu')) - self.register_buffer("accumulator_filled", torch.zeros(1, dtype=torch.long, device='cpu')) - self.register_buffer("accumulator", torch.zeros(store_past, discrete_bins)) - else: - self.record_past = False - - def forward(self, x): - other_dims = set(range(len(x.shape)))-set([self.dim]) - averaged = x.sum(dim=tuple(other_dims)) / x.sum() - averaged = averaged - averaged.mean() - - if self.record_past: - acc_count = self.accumulator.shape[0] - avg = averaged.detach().clone() - if self.accumulator_filled > 0: - averaged = torch.mean(self.accumulator, dim=0) * (acc_count-1) / acc_count + \ - averaged / acc_count - - # Also push averaged into the accumulator. - self.accumulator[self.accumulator_index] = avg - self.accumulator_index += 1 - if self.accumulator_index >= acc_count: - self.accumulator_index *= 0 - if self.accumulator_filled <= 0: - self.accumulator_filled += 1 - - return torch.sum(-self.dist.log_prob(averaged)) - - -class ResBlock(nn.Module): - def __init__(self, chan, conv, activation): - super().__init__() - self.net = nn.Sequential( - conv(chan, chan, 3, padding = 1), - activation(), - conv(chan, chan, 3, padding = 1), - activation(), - conv(chan, chan, 1) - ) - - def forward(self, x): - return self.net(x) + x - - -class UpsampledConv(nn.Module): - def __init__(self, conv, *args, **kwargs): - super().__init__() - assert 'stride' in kwargs.keys() - self.stride = kwargs['stride'] - del kwargs['stride'] - self.conv = conv(*args, **kwargs) - - def forward(self, x): - up = nn.functional.interpolate(x, scale_factor=self.stride, mode='nearest') - return self.conv(up) - - -# DiscreteVAE partially derived from lucidrains DALLE implementation -# Credit: https://github.com/lucidrains/DALLE-pytorch -class DiscreteVAE(nn.Module): - def __init__( - self, - positional_dims=2, - num_tokens = 512, - codebook_dim = 512, - num_layers = 3, - num_resnet_blocks = 0, - hidden_dim = 64, - channels = 3, - stride = 2, - kernel_size = 4, - use_transposed_convs = True, - encoder_norm = False, - activation = 'relu', - smooth_l1_loss = False, - straight_through = False, - normalization = None, # ((0.5,) * 3, (0.5,) * 3), - record_codes = False, - discretization_loss_averaging_steps = 100, - lr_quantizer_args = {}, - ): - super().__init__() - has_resblocks = num_resnet_blocks > 0 - - self.num_tokens = num_tokens - self.num_layers = num_layers - self.straight_through = straight_through - self.positional_dims = positional_dims - self.discrete_loss = DiscretizationLoss(num_tokens, 2, 1 / (num_tokens*2), discretization_loss_averaging_steps) - - assert positional_dims > 0 and positional_dims < 3 # This VAE only supports 1d and 2d inputs for now. - if positional_dims == 2: - conv = nn.Conv2d - conv_transpose = nn.ConvTranspose2d - else: - conv = nn.Conv1d - conv_transpose = nn.ConvTranspose1d - if not use_transposed_convs: - conv_transpose = functools.partial(UpsampledConv, conv) - - if activation == 'relu': - act = nn.ReLU - elif activation == 'silu': - act = nn.SiLU - else: - assert NotImplementedError() - - - enc_layers = [] - dec_layers = [] - - if num_layers > 0: - enc_chans = [hidden_dim * 2 ** i for i in range(num_layers)] - dec_chans = list(reversed(enc_chans)) - - enc_chans = [channels, *enc_chans] - - dec_init_chan = codebook_dim if not has_resblocks else dec_chans[0] - dec_chans = [dec_init_chan, *dec_chans] - - enc_chans_io, dec_chans_io = map(lambda t: list(zip(t[:-1], t[1:])), (enc_chans, dec_chans)) - - pad = (kernel_size - 1) // 2 - for (enc_in, enc_out), (dec_in, dec_out) in zip(enc_chans_io, dec_chans_io): - enc_layers.append(nn.Sequential(conv(enc_in, enc_out, kernel_size, stride = stride, padding = pad), act())) - if encoder_norm: - enc_layers.append(nn.GroupNorm(8, enc_out)) - dec_layers.append(nn.Sequential(conv_transpose(dec_in, dec_out, kernel_size, stride = stride, padding = pad), act())) - dec_out_chans = dec_chans[-1] - innermost_dim = dec_chans[0] - else: - enc_layers.append(nn.Sequential(conv(channels, hidden_dim, 1), act())) - dec_out_chans = hidden_dim - innermost_dim = hidden_dim - - for _ in range(num_resnet_blocks): - dec_layers.insert(0, ResBlock(innermost_dim, conv, act)) - enc_layers.append(ResBlock(innermost_dim, conv, act)) - - if num_resnet_blocks > 0: - dec_layers.insert(0, conv(codebook_dim, innermost_dim, 1)) - - - enc_layers.append(conv(innermost_dim, codebook_dim, 1)) - dec_layers.append(conv(dec_out_chans, channels, 1)) - - self.encoder = nn.Sequential(*enc_layers) - self.decoder = nn.Sequential(*dec_layers) - - self.loss_fn = F.smooth_l1_loss if smooth_l1_loss else F.mse_loss - self.codebook = Quantize(codebook_dim, num_tokens, new_return_order=True) - - # take care of normalization within class - self.normalization = normalization - self.record_codes = record_codes - if record_codes: - self.codes = torch.zeros((1228800,), dtype=torch.long) - self.code_ind = 0 - self.total_codes = 0 - self.internal_step = 0 - - def norm(self, images): - if not self.normalization is not None: - return images - - means, stds = map(lambda t: torch.as_tensor(t).to(images), self.normalization) - arrange = 'c -> () c () ()' if self.positional_dims == 2 else 'c -> () c ()' - means, stds = map(lambda t: rearrange(t, arrange), (means, stds)) - images = images.clone() - images.sub_(means).div_(stds) - return images - - def get_debug_values(self, step, __): - if self.record_codes and self.total_codes > 0: - # Report annealing schedule - return {'histogram_codes': self.codes[:self.total_codes]} - else: - return {} - - @torch.no_grad() - @eval_decorator - def get_codebook_indices(self, images): - img = self.norm(images) - logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) - sampled, codes, _ = self.codebook(logits) - self.log_codes(codes) - return codes - - def decode( - self, - img_seq - ): - self.log_codes(img_seq) - if hasattr(self.codebook, 'embed_code'): - image_embeds = self.codebook.embed_code(img_seq) - else: - image_embeds = F.embedding(img_seq, self.codebook.codebook) - b, n, d = image_embeds.shape - - kwargs = {} - if self.positional_dims == 1: - arrange = 'b n d -> b d n' - else: - h = w = int(sqrt(n)) - arrange = 'b (h w) d -> b d h w' - kwargs = {'h': h, 'w': w} - image_embeds = rearrange(image_embeds, arrange, **kwargs) - images = [image_embeds] - for layer in self.decoder: - images.append(layer(images[-1])) - return images[-1], images[-2] - - def infer(self, img): - img = self.norm(img) - logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) - sampled, codes, commitment_loss = self.codebook(logits) - return self.decode(codes) - - # Note: This module is not meant to be run in forward() except while training. It has special logic which performs - # evaluation using quantized values when it detects that it is being run in eval() mode, which will be substantially - # more lossy (but useful for determining network performance). - def forward( - self, - img - ): - img = self.norm(img) - logits = self.encoder(img).permute((0,2,3,1) if len(img.shape) == 4 else (0,2,1)) - sampled, codes, commitment_loss = self.codebook(logits) - sampled = sampled.permute((0,3,1,2) if len(img.shape) == 4 else (0,2,1)) - - if self.training: - out = sampled - for d in self.decoder: - out = d(out) - self.log_codes(codes) - else: - # This is non-differentiable, but gives a better idea of how the network is actually performing. - out, _ = self.decode(codes) - - # reconstruction loss - recon_loss = self.loss_fn(img, out, reduction='none') - - return recon_loss, commitment_loss, out - - def log_codes(self, codes): - # This is so we can debug the distribution of codes being learned. - if self.record_codes and self.internal_step % 10 == 0: - codes = codes.flatten() - l = codes.shape[0] - i = self.code_ind if (self.codes.shape[0] - self.code_ind) > l else self.codes.shape[0] - l - self.codes[i:i+l] = codes.cpu() - self.code_ind = self.code_ind + l - if self.code_ind >= self.codes.shape[0]: - self.code_ind = 0 - self.total_codes += 1 - self.internal_step += 1 - - -if __name__ == '__main__': - v = DiscreteVAE(channels=80, normalization=None, positional_dims=1, num_tokens=8192, codebook_dim=2048, - hidden_dim=512, num_resnet_blocks=3, kernel_size=3, num_layers=1, use_transposed_convs=False) - r,l,o=v(torch.randn(1,80,256)) - v.decode(torch.randint(0,8192,(1,256))) - print(o.shape, l.shape) diff --git a/models/vocoder.py b/models/vocoder.py new file mode 100644 index 0000000..d38fb56 --- /dev/null +++ b/models/vocoder.py @@ -0,0 +1,325 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +MAX_WAV_VALUE = 32768.0 + +class KernelPredictor(torch.nn.Module): + ''' Kernel predictor for the location-variable convolutions''' + + def __init__( + self, + cond_channels, + conv_in_channels, + conv_out_channels, + conv_layers, + conv_kernel_size=3, + kpnet_hidden_channels=64, + kpnet_conv_size=3, + kpnet_dropout=0.0, + kpnet_nonlinear_activation="LeakyReLU", + kpnet_nonlinear_activation_params={"negative_slope": 0.1}, + ): + ''' + Args: + cond_channels (int): number of channel for the conditioning sequence, + conv_in_channels (int): number of channel for the input sequence, + conv_out_channels (int): number of channel for the output sequence, + conv_layers (int): number of layers + ''' + super().__init__() + + self.conv_in_channels = conv_in_channels + self.conv_out_channels = conv_out_channels + self.conv_kernel_size = conv_kernel_size + self.conv_layers = conv_layers + + kpnet_kernel_channels = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers # l_w + kpnet_bias_channels = conv_out_channels * conv_layers # l_b + + self.input_conv = nn.Sequential( + nn.utils.weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)), + getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + ) + + self.residual_convs = nn.ModuleList() + padding = (kpnet_conv_size - 1) // 2 + for _ in range(3): + self.residual_convs.append( + nn.Sequential( + nn.Dropout(kpnet_dropout), + nn.utils.weight_norm( + nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, + bias=True)), + getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + nn.utils.weight_norm( + nn.Conv1d(kpnet_hidden_channels, kpnet_hidden_channels, kpnet_conv_size, padding=padding, + bias=True)), + getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params), + ) + ) + self.kernel_conv = nn.utils.weight_norm( + nn.Conv1d(kpnet_hidden_channels, kpnet_kernel_channels, kpnet_conv_size, padding=padding, bias=True)) + self.bias_conv = nn.utils.weight_norm( + nn.Conv1d(kpnet_hidden_channels, kpnet_bias_channels, kpnet_conv_size, padding=padding, bias=True)) + + def forward(self, c): + ''' + Args: + c (Tensor): the conditioning sequence (batch, cond_channels, cond_length) + ''' + batch, _, cond_length = c.shape + c = self.input_conv(c) + for residual_conv in self.residual_convs: + residual_conv.to(c.device) + c = c + residual_conv(c) + k = self.kernel_conv(c) + b = self.bias_conv(c) + kernels = k.contiguous().view( + batch, + self.conv_layers, + self.conv_in_channels, + self.conv_out_channels, + self.conv_kernel_size, + cond_length, + ) + bias = b.contiguous().view( + batch, + self.conv_layers, + self.conv_out_channels, + cond_length, + ) + + return kernels, bias + + def remove_weight_norm(self): + nn.utils.remove_weight_norm(self.input_conv[0]) + nn.utils.remove_weight_norm(self.kernel_conv) + nn.utils.remove_weight_norm(self.bias_conv) + for block in self.residual_convs: + nn.utils.remove_weight_norm(block[1]) + nn.utils.remove_weight_norm(block[3]) + + +class LVCBlock(torch.nn.Module): + '''the location-variable convolutions''' + + def __init__( + self, + in_channels, + cond_channels, + stride, + dilations=[1, 3, 9, 27], + lReLU_slope=0.2, + conv_kernel_size=3, + cond_hop_length=256, + kpnet_hidden_channels=64, + kpnet_conv_size=3, + kpnet_dropout=0.0, + ): + super().__init__() + + self.cond_hop_length = cond_hop_length + self.conv_layers = len(dilations) + self.conv_kernel_size = conv_kernel_size + + self.kernel_predictor = KernelPredictor( + cond_channels=cond_channels, + conv_in_channels=in_channels, + conv_out_channels=2 * in_channels, + conv_layers=len(dilations), + conv_kernel_size=conv_kernel_size, + kpnet_hidden_channels=kpnet_hidden_channels, + kpnet_conv_size=kpnet_conv_size, + kpnet_dropout=kpnet_dropout, + kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope} + ) + + self.convt_pre = nn.Sequential( + nn.LeakyReLU(lReLU_slope), + nn.utils.weight_norm(nn.ConvTranspose1d(in_channels, in_channels, 2 * stride, stride=stride, + padding=stride // 2 + stride % 2, output_padding=stride % 2)), + ) + + self.conv_blocks = nn.ModuleList() + for dilation in dilations: + self.conv_blocks.append( + nn.Sequential( + nn.LeakyReLU(lReLU_slope), + nn.utils.weight_norm(nn.Conv1d(in_channels, in_channels, conv_kernel_size, + padding=dilation * (conv_kernel_size - 1) // 2, dilation=dilation)), + nn.LeakyReLU(lReLU_slope), + ) + ) + + def forward(self, x, c): + ''' forward propagation of the location-variable convolutions. + Args: + x (Tensor): the input sequence (batch, in_channels, in_length) + c (Tensor): the conditioning sequence (batch, cond_channels, cond_length) + + Returns: + Tensor: the output sequence (batch, in_channels, in_length) + ''' + _, in_channels, _ = x.shape # (B, c_g, L') + + x = self.convt_pre(x) # (B, c_g, stride * L') + kernels, bias = self.kernel_predictor(c) + + for i, conv in enumerate(self.conv_blocks): + output = conv(x) # (B, c_g, stride * L') + + k = kernels[:, i, :, :, :, :] # (B, 2 * c_g, c_g, kernel_size, cond_length) + b = bias[:, i, :, :] # (B, 2 * c_g, cond_length) + + output = self.location_variable_convolution(output, k, b, + hop_size=self.cond_hop_length) # (B, 2 * c_g, stride * L'): LVC + x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh( + output[:, in_channels:, :]) # (B, c_g, stride * L'): GAU + + return x + + def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256): + ''' perform location-variable convolution operation on the input sequence (x) using the local convolution kernl. + Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100. + Args: + x (Tensor): the input sequence (batch, in_channels, in_length). + kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length) + bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length) + dilation (int): the dilation of convolution. + hop_size (int): the hop_size of the conditioning sequence. + Returns: + (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length). + ''' + batch, _, in_length = x.shape + batch, _, out_channels, kernel_size, kernel_length = kernel.shape + assert in_length == (kernel_length * hop_size), "length of (x, kernel) is not matched" + + padding = dilation * int((kernel_size - 1) / 2) + x = F.pad(x, (padding, padding), 'constant', 0) # (batch, in_channels, in_length + 2*padding) + x = x.unfold(2, hop_size + 2 * padding, hop_size) # (batch, in_channels, kernel_length, hop_size + 2*padding) + + if hop_size < dilation: + x = F.pad(x, (0, dilation), 'constant', 0) + x = x.unfold(3, dilation, + dilation) # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation) + x = x[:, :, :, :, :hop_size] + x = x.transpose(3, 4) # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation) + x = x.unfold(4, kernel_size, 1) # (batch, in_channels, kernel_length, dilation, _, kernel_size) + + o = torch.einsum('bildsk,biokl->bolsd', x, kernel) + o = o.to(memory_format=torch.channels_last_3d) + bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d) + o = o + bias + o = o.contiguous().view(batch, out_channels, -1) + + return o + + def remove_weight_norm(self): + self.kernel_predictor.remove_weight_norm() + nn.utils.remove_weight_norm(self.convt_pre[1]) + for block in self.conv_blocks: + nn.utils.remove_weight_norm(block[1]) + + +class UnivNetGenerator(nn.Module): + """UnivNet Generator""" + + def __init__(self, noise_dim=64, channel_size=32, dilations=[1,3,9,27], strides=[8,8,4], lReLU_slope=.2, kpnet_conv_size=3, + # Below are MEL configurations options that this generator requires. + hop_length=256, n_mel_channels=100): + super(UnivNetGenerator, self).__init__() + self.mel_channel = n_mel_channels + self.noise_dim = noise_dim + self.hop_length = hop_length + channel_size = channel_size + kpnet_conv_size = kpnet_conv_size + + self.res_stack = nn.ModuleList() + hop_length = 1 + for stride in strides: + hop_length = stride * hop_length + self.res_stack.append( + LVCBlock( + channel_size, + n_mel_channels, + stride=stride, + dilations=dilations, + lReLU_slope=lReLU_slope, + cond_hop_length=hop_length, + kpnet_conv_size=kpnet_conv_size + ) + ) + + self.conv_pre = \ + nn.utils.weight_norm(nn.Conv1d(noise_dim, channel_size, 7, padding=3, padding_mode='reflect')) + + self.conv_post = nn.Sequential( + nn.LeakyReLU(lReLU_slope), + nn.utils.weight_norm(nn.Conv1d(channel_size, 1, 7, padding=3, padding_mode='reflect')), + nn.Tanh(), + ) + + def forward(self, c, z): + ''' + Args: + c (Tensor): the conditioning sequence of mel-spectrogram (batch, mel_channels, in_length) + z (Tensor): the noise sequence (batch, noise_dim, in_length) + + ''' + z = self.conv_pre(z) # (B, c_g, L) + + for res_block in self.res_stack: + res_block.to(z.device) + z = res_block(z, c) # (B, c_g, L * s_0 * ... * s_i) + + z = self.conv_post(z) # (B, 1, L * 256) + + return z + + def eval(self, inference=False): + super(UnivNetGenerator, self).eval() + # don't remove weight norm while validation in training loop + if inference: + self.remove_weight_norm() + + def remove_weight_norm(self): + print('Removing weight norm...') + + nn.utils.remove_weight_norm(self.conv_pre) + + for layer in self.conv_post: + if len(layer.state_dict()) != 0: + nn.utils.remove_weight_norm(layer) + + for res_block in self.res_stack: + res_block.remove_weight_norm() + + def inference(self, c, z=None): + # pad input mel with zeros to cut artifact + # see https://github.com/seungwonpark/melgan/issues/8 + zero = torch.full((c.shape[0], self.mel_channel, 10), -11.5129).to(c.device) + mel = torch.cat((c, zero), dim=2) + + if z is None: + z = torch.randn(c.shape[0], self.noise_dim, mel.size(2)).to(mel.device) + + audio = self.forward(mel, z) + audio = audio[:, :, :-(self.hop_length * 10)] + audio = audio.clamp(min=-1, max=1) + return audio + + +if __name__ == '__main__': + model = UnivNetGenerator() + + c = torch.randn(3, 100, 10) + z = torch.randn(3, 64, 10) + print(c.shape) + + y = model(c, z) + print(y.shape) + assert y.shape == torch.Size([3, 1, 2560]) + + pytorch_total_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + print(pytorch_total_params) diff --git a/requirements.txt b/requirements.txt index 880c033..568575c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,5 @@ tokenizers inflect progressbar einops -unidecode \ No newline at end of file +unidecode +x-transformers \ No newline at end of file diff --git a/utils/audio.py b/utils/audio.py index 22a2506..ff2f4ea 100644 --- a/utils/audio.py +++ b/utils/audio.py @@ -3,6 +3,8 @@ import torchaudio import numpy as np from scipy.io.wavfile import read +from utils.stft import STFT + def load_wav_to_torch(full_path): sampling_rate, data = read(full_path) @@ -43,4 +45,86 @@ def load_audio(audiopath, sampling_rate): print(f"Error with {audiopath}. Max={audio.max()} min={audio.min()}") audio.clip_(-1, 1) - return audio.unsqueeze(0) \ No newline at end of file + return audio.unsqueeze(0) + + +TACOTRON_MEL_MAX = 2.3143386840820312 +TACOTRON_MEL_MIN = -11.512925148010254 + + +def denormalize_tacotron_mel(norm_mel): + return ((norm_mel+1)/2)*(TACOTRON_MEL_MAX-TACOTRON_MEL_MIN)+TACOTRON_MEL_MIN + + +def normalize_tacotron_mel(mel): + return 2 * ((mel - TACOTRON_MEL_MIN) / (TACOTRON_MEL_MAX - TACOTRON_MEL_MIN)) - 1 + + +def dynamic_range_compression(x, C=1, clip_val=1e-5): + """ + PARAMS + ------ + C: compression factor + """ + return torch.log(torch.clamp(x, min=clip_val) * C) + + +def dynamic_range_decompression(x, C=1): + """ + PARAMS + ------ + C: compression factor used to compress + """ + return torch.exp(x) / C + + +class TacotronSTFT(torch.nn.Module): + def __init__(self, filter_length=1024, hop_length=256, win_length=1024, + n_mel_channels=80, sampling_rate=22050, mel_fmin=0.0, + mel_fmax=8000.0): + super(TacotronSTFT, self).__init__() + self.n_mel_channels = n_mel_channels + self.sampling_rate = sampling_rate + self.stft_fn = STFT(filter_length, hop_length, win_length) + from librosa.filters import mel as librosa_mel_fn + mel_basis = librosa_mel_fn( + sampling_rate, filter_length, n_mel_channels, mel_fmin, mel_fmax) + mel_basis = torch.from_numpy(mel_basis).float() + self.register_buffer('mel_basis', mel_basis) + + def spectral_normalize(self, magnitudes): + output = dynamic_range_compression(magnitudes) + return output + + def spectral_de_normalize(self, magnitudes): + output = dynamic_range_decompression(magnitudes) + return output + + def mel_spectrogram(self, y): + """Computes mel-spectrograms from a batch of waves + PARAMS + ------ + y: Variable(torch.FloatTensor) with shape (B, T) in range [-1, 1] + + RETURNS + ------- + mel_output: torch.FloatTensor of shape (B, n_mel_channels, T) + """ + assert(torch.min(y.data) >= -10) + assert(torch.max(y.data) <= 10) + y = torch.clip(y, min=-1, max=1) + + magnitudes, phases = self.stft_fn.transform(y) + magnitudes = magnitudes.data + mel_output = torch.matmul(self.mel_basis, magnitudes) + mel_output = self.spectral_normalize(mel_output) + return mel_output + + +def wav_to_univnet_mel(wav, do_normalization=False): + stft = TacotronSTFT(1024, 256, 1024, 100, 24000, 0, 12000) + stft = stft.cuda() + mel = stft.mel_spectrogram(wav) + if do_normalization: + mel = normalize_tacotron_mel(mel) + return mel \ No newline at end of file diff --git a/utils/diffusion.py b/utils/diffusion.py index acd5633..0be58ae 100644 --- a/utils/diffusion.py +++ b/utils/diffusion.py @@ -197,11 +197,17 @@ class GaussianDiffusion: model_var_type, loss_type, rescale_timesteps=False, + conditioning_free=False, + conditioning_free_k=1, + ramp_conditioning_free=True, ): self.model_mean_type = ModelMeanType(model_mean_type) self.model_var_type = ModelVarType(model_var_type) self.loss_type = LossType(loss_type) self.rescale_timesteps = rescale_timesteps + self.conditioning_free = conditioning_free + self.conditioning_free_k = conditioning_free_k + self.ramp_conditioning_free = ramp_conditioning_free # Use float64 for accuracy. betas = np.array(betas, dtype=np.float64) @@ -332,10 +338,14 @@ class GaussianDiffusion: B, C = x.shape[:2] assert t.shape == (B,) model_output = model(x, self._scale_timesteps(t), **model_kwargs) + if self.conditioning_free: + model_output_no_conditioning = model(x, self._scale_timesteps(t), conditioning_free=True, **model_kwargs) if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]: assert model_output.shape == (B, C * 2, *x.shape[2:]) model_output, model_var_values = th.split(model_output, C, dim=1) + if self.conditioning_free: + model_output_no_conditioning, _ = th.split(model_output_no_conditioning, C, dim=1) if self.model_var_type == ModelVarType.LEARNED: model_log_variance = model_var_values model_variance = th.exp(model_log_variance) @@ -364,6 +374,14 @@ class GaussianDiffusion: model_variance = _extract_into_tensor(model_variance, t, x.shape) model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape) + if self.conditioning_free: + if self.ramp_conditioning_free: + assert t.shape[0] == 1 # This should only be used in inference. + cfk = self.conditioning_free_k * (1 - self._scale_timesteps(t)[0].item() / self.num_timesteps) + else: + cfk = self.conditioning_free_k + model_output = (1 + cfk) * model_output - cfk * model_output_no_conditioning + def process_xstart(x): if denoised_fn is not None: x = denoised_fn(x) diff --git a/utils/stft.py b/utils/stft.py new file mode 100644 index 0000000..8de6bfb --- /dev/null +++ b/utils/stft.py @@ -0,0 +1,193 @@ +""" +BSD 3-Clause License + +Copyright (c) 2017, Prem Seetharaman +All rights reserved. + +* Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR +ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES +(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; +LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON +ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +""" + +import torch +import numpy as np +import torch.nn.functional as F +from torch.autograd import Variable +from scipy.signal import get_window +from librosa.util import pad_center, tiny +import librosa.util as librosa_util + + +def window_sumsquare(window, n_frames, hop_length=200, win_length=800, + n_fft=800, dtype=np.float32, norm=None): + """ + # from librosa 0.6 + Compute the sum-square envelope of a window function at a given hop length. + + This is used to estimate modulation effects induced by windowing + observations in short-time fourier transforms. + + Parameters + ---------- + window : string, tuple, number, callable, or list-like + Window specification, as in `get_window` + + n_frames : int > 0 + The number of analysis frames + + hop_length : int > 0 + The number of samples to advance between frames + + win_length : [optional] + The length of the window function. By default, this matches `n_fft`. + + n_fft : int > 0 + The length of each analysis frame. + + dtype : np.dtype + The data type of the output + + Returns + ------- + wss : np.ndarray, shape=`(n_fft + hop_length * (n_frames - 1))` + The sum-squared envelope of the window function + """ + if win_length is None: + win_length = n_fft + + n = n_fft + hop_length * (n_frames - 1) + x = np.zeros(n, dtype=dtype) + + # Compute the squared window at the desired length + win_sq = get_window(window, win_length, fftbins=True) + win_sq = librosa_util.normalize(win_sq, norm=norm)**2 + win_sq = librosa_util.pad_center(win_sq, n_fft) + + # Fill the envelope + for i in range(n_frames): + sample = i * hop_length + x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))] + return x + + +class STFT(torch.nn.Module): + """adapted from Prem Seetharaman's https://github.com/pseeth/pytorch-stft""" + def __init__(self, filter_length=800, hop_length=200, win_length=800, + window='hann'): + super(STFT, self).__init__() + self.filter_length = filter_length + self.hop_length = hop_length + self.win_length = win_length + self.window = window + self.forward_transform = None + scale = self.filter_length / self.hop_length + fourier_basis = np.fft.fft(np.eye(self.filter_length)) + + cutoff = int((self.filter_length / 2 + 1)) + fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]), + np.imag(fourier_basis[:cutoff, :])]) + + forward_basis = torch.FloatTensor(fourier_basis[:, None, :]) + inverse_basis = torch.FloatTensor( + np.linalg.pinv(scale * fourier_basis).T[:, None, :]) + + if window is not None: + assert(filter_length >= win_length) + # get window and zero center pad it to filter_length + fft_window = get_window(window, win_length, fftbins=True) + fft_window = pad_center(fft_window, filter_length) + fft_window = torch.from_numpy(fft_window).float() + + # window the bases + forward_basis *= fft_window + inverse_basis *= fft_window + + self.register_buffer('forward_basis', forward_basis.float()) + self.register_buffer('inverse_basis', inverse_basis.float()) + + def transform(self, input_data): + num_batches = input_data.size(0) + num_samples = input_data.size(1) + + self.num_samples = num_samples + + # similar to librosa, reflect-pad the input + input_data = input_data.view(num_batches, 1, num_samples) + input_data = F.pad( + input_data.unsqueeze(1), + (int(self.filter_length / 2), int(self.filter_length / 2), 0, 0), + mode='reflect') + input_data = input_data.squeeze(1) + + forward_transform = F.conv1d( + input_data, + Variable(self.forward_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + cutoff = int((self.filter_length / 2) + 1) + real_part = forward_transform[:, :cutoff, :] + imag_part = forward_transform[:, cutoff:, :] + + magnitude = torch.sqrt(real_part**2 + imag_part**2) + phase = torch.autograd.Variable( + torch.atan2(imag_part.data, real_part.data)) + + return magnitude, phase + + def inverse(self, magnitude, phase): + recombine_magnitude_phase = torch.cat( + [magnitude*torch.cos(phase), magnitude*torch.sin(phase)], dim=1) + + inverse_transform = F.conv_transpose1d( + recombine_magnitude_phase, + Variable(self.inverse_basis, requires_grad=False), + stride=self.hop_length, + padding=0) + + if self.window is not None: + window_sum = window_sumsquare( + self.window, magnitude.size(-1), hop_length=self.hop_length, + win_length=self.win_length, n_fft=self.filter_length, + dtype=np.float32) + # remove modulation effects + approx_nonzero_indices = torch.from_numpy( + np.where(window_sum > tiny(window_sum))[0]) + window_sum = torch.autograd.Variable( + torch.from_numpy(window_sum), requires_grad=False) + window_sum = window_sum.cuda() if magnitude.is_cuda else window_sum + inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices] + + # scale by hop ratio + inverse_transform *= float(self.filter_length) / self.hop_length + + inverse_transform = inverse_transform[:, :, int(self.filter_length/2):] + inverse_transform = inverse_transform[:, :, :-int(self.filter_length/2):] + + return inverse_transform + + def forward(self, input_data): + self.magnitude, self.phase = self.transform(input_data) + reconstruction = self.inverse(self.magnitude, self.phase) + return reconstruction \ No newline at end of file